Cost-effective reflectance calibration method for small UAV images

Jeong, Yongsik; Yu, Jaehyung; Wang, Lei; Shin, Haein; Koh, Sang‐Mo; Park, Gyesoon

doi:10.1080/01431161.2018.1516307

Cited by 20 publications

(13 citation statements)

References 31 publications

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“…While the multispectral imagery produced a linear empirical line, an exponential relationship was required for the RGB imagery. An exponential relationship of the empirical line correction for RGB UAV imagery was also identified by Jeong et al ( 2018 ).…”

Section: Methodsmentioning

confidence: 60%

“…In fact, the use of high-quality field data is imperative for both calibration and validation to standardize UAV-derived outputs suited as model input for prediction of crop parameters (Von Bueren et al, 2014 ). Radiometric correction of imagery is equally important to ensure consistency of optical image data over time and between sites (Jeong et al, 2018 ; Tmusic et al, 2020 ). Hence, despite the large array of UAV image data sets and acquisition and processing routines for crop assessment, potential avenues exist to improve the consistency of diverse data used as input for machine learning approaches.…”

Section: Discussionmentioning

confidence: 99%

“…However, it is considered good practice to radiometrically calibrate UAV imagery to remove or normalize effects of sun-object-sensor geometry and illumination conditions (Tmusic et al, 2020 ). The empirical line calibration method has become a standard approach for UAV-based studies to correct the imagery to at-surface reflectance using either natural features (Lelong et al, 2008 ) or other types of commercial (e.g., spectralon) or more cost-effective Lambertian panels, e.g., made from masonite, plywood, or ethylene-vinyl acetate (Wang and Myint, 2015 ; Jeong et al, 2018 ; Barreto et al, 2019 ; Tmusic et al, 2020 ). An empirical line correction, produced between field-derived spectrometer measurements and the digital numbers of the radiometric calibration panels within the multispectral and RGB orthomosaics, was used to convert the orthomosaics to at-surface reflectance (Ahmed et al, 2017 ; Johansen et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

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Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest

Johansen

Morton

Malbéteau

et al. 2020

Front. Artif. Intell.

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Biomass and yield are key variables for assessing the production and performance of agricultural systems. Modeling and predicting the biomass and yield of individual plants at the farm scale represents a major challenge in precision agriculture, particularly when salinity and other abiotic stresses may play a role. Here, we evaluate a diversity panel of the wild tomato species ( Solanum pimpinellifolium ) through both field and unmanned aerial vehicle (UAV)-based phenotyping of 600 control and 600 salt-treated plants. The study objective was to predict fresh shoot mass, tomato fruit numbers, and yield mass at harvest based on a range of variables derived from the UAV imagery. UAV-based red–green–blue (RGB) imageries collected 1, 2, 4, 6, 7, and 8 weeks before harvest were also used to determine if prediction accuracies varied between control and salt-treated plants. Multispectral UAV-based imagery was also collected 1 and 2 weeks prior to harvest to further explore predictive insights. In order to estimate the end of season biomass and yield, a random forest machine learning approach was implemented using UAV-imagery-derived predictors as input variables. Shape features derived from the UAV, such as plant area, border length, width, and length, were found to have the highest importance in the predictions, followed by vegetation indices and the entropy texture measure. The multispectral UAV imagery collected 2 weeks prior to harvest produced the highest explained variances for fresh shoot mass (87.95%), fruit numbers (63.88%), and yield mass per plant (66.51%). The RGB UAV imagery produced very similar results to those of the multispectral UAV dataset, with the explained variance reducing as a function of increasing time to harvest. The results showed that predicting the yield of salt-stressed plants produced higher accuracies when the models excluded control plants, whereas predicting the yield of control plants was not affected by the inclusion of salt-stressed plants within the models. This research demonstrates that it is possible to predict the average biomass and yield up to 8 weeks prior to harvest within 4.23% of field-based measurements and up to 4 weeks prior to harvest at the individual plant level. Results from this work may be useful in providing guidance for yield forecasting of healthy and salt-stressed tomato plants, which in turn may inform growing practices, logistical planning, and sales operations.

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Section: Methodsmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest

Johansen

Morton

Malbéteau

et al. 2020

Front. Artif. Intell.

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“…These indices were thus sensitive to the camera settings and to the prevailing environmental conditions at the time of the flight. This sensitivity can possibly be reduced by calibration of the sensor signal and conversion to reflectance values using grey reference panels [80]. However, even without this correction, several other VIs were quite robust to the different sensor settings and conditions, and maintained high correlations with the breeder score when data from all observation days were pooled (Figure 4).…”

Section: Research Question 1: Visual-based Indices Are Good Proxies Fmentioning

confidence: 99%

Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

Swaef¹,

Maes

Aper³

et al. 2021

Remote Sensing

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The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant.

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“…Flight altitudes of small UAS (sUAS) typically do not exceed 250 meters due to governmental restrictions on UAS operations [34]. As a result, numerous UAS studies have implemented an empirical line method (ELM) for radiometric calibration of UAS imagery [35,36,[45][46][47][37][38][39][40][41][42][43][44]. The foundation of an ELM is the regression of ground-based spectroradiometer measurements and airborne remotely sensed measurements of a spectrally stable calibration target to develop calibration equations that convert image digital numbers (DNs) to physical at-surface reflectance units [48].…”

Section: Introductionmentioning

confidence: 99%

Development of a Simplified Radiometric Calibration Framework for Water-Based and Rapid Deployment Unmanned Aerial System (UAS) Operations

Zarzar¹,

Dash²,

Dyer³

et al. 2020

Preprint

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The current study sets out to develop an empirical line method (ELM) radiometric calibration framework for reducing atmospheric contributions in UAS imagery and for producing scaled remote sensing reflectance imagery. Using a MicaSense RedEdge camera flown on a custom-built octocopter, the research reported herein finds that atmospheric contributions have an important impact on UAS imagery. Data collected over the Lower Pearl River Estuary in Mississippi during five week-long missions covering a wide range of environmental conditions was used to develop and test a simplified ELM radiometric calibration framework designed specifically for the reduction of atmospheric contributions to UAS imagery in studies with limited site accessibility or data acquisition time constraints. The framework was effective in reducing atmospheric and other external contributions to UAS imagery. Unique to the proposed radiometric calibration framework is the radiance to reflectance conversion conducted externally from the calibration equations which allows for the normalization of illumination independent from the time of UAS image acquisition and from the time of calibration equations development. This paper presents the simplified ELM radiometric calibration framework that can be used as a time-effective calibration technique to reduce errors in the UAS imagery.

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Cost-effective reflectance calibration method for small UAV images

Cited by 20 publications

References 31 publications

Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest

Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest

Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

Development of a Simplified Radiometric Calibration Framework for Water-Based and Rapid Deployment Unmanned Aerial System (UAS) Operations

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